Point Lepreau Refurbishment Project Level 2 PSA Applications
Bibliographic record
Abstract
A Probabilistic Safety Assessment (PSA) for Point Lepreau Generating Station has been completed as part of the plant Refurbishment Project. The main objective of this PSA is to provide insights into plant safety design and performance, including the identification of dominant risk contributors and assessing options for reducing risk. The scope of this assessment covers Level 1 and 2 PSA and includes internal events for full power and shutdown, internal fires and internal floods as well as PSA-based seismic margin assessment (SMA) for full power operation. Following the accident sequence quantification for internal events, fire and flood, the results were integrated to provide an overall estimation of the Severe Core Damage Frequency (SCDF) and the Large Release Frequency (LRF) for the refurbished Point Lepreau plant. Importance analysis was performed on the integrated results to identify risk-significant failures, using Fussell-Vesely and Risk Achievement Worth indices, and risk-contributors using Risk Reduction Worth indices. Based on the importance measures, analysis was performed to evaluate the sensitivity of the SCDF and LRF results to the dominant contributors. Uncertainty analysis was also performed to provide qualitative discussions and quantitative measures of the uncertainties in the results of the PSA, namely the frequency of severe core damage or external releases. Based on the results, recommendations were made to improve maintenance, testing, training procedures as well as housekeeping. Also, results of the Level 1 and 2 PSA have been used to determine the safety important systems and components. This paper discusses the key results and recommendations of the Level 2 PSA as well as the methodology used to determine the safety important systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".